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Region-based moving object detection using spatially conditioned nonparametric models in a GPU
Cita
Berjón Díez, Daniel 
ORCID: https://orcid.org/0000-0003-0584-7166, Cuevas Rodríguez, Carlos ORCID: https://orcid.org/0000-0001-9873-8502, Morán Burgos, Francisco ORCID: https://orcid.org/0000-0003-3837-692X and García Santos, Narciso ORCID: https://orcid.org/0000-0002-0397-894X
(2014).
Region-based moving object detection using spatially conditioned nonparametric models in a GPU.
En: "IEEE International Conference on Consumer Electronics (ICCE 2014)", 10/01/2014 - 13/01/2014, Las Vegas, Nevada, USA. pp. 359-360.
ORCID: https://orcid.org/0000-0003-0584-7166, Cuevas Rodríguez, Carlos ORCID: https://orcid.org/0000-0001-9873-8502, Morán Burgos, Francisco ORCID: https://orcid.org/0000-0003-3837-692X and García Santos, Narciso ORCID: https://orcid.org/0000-0002-0397-894X
(2014).
Region-based moving object detection using spatially conditioned nonparametric models in a GPU.
En: "IEEE International Conference on Consumer Electronics (ICCE 2014)", 10/01/2014 - 13/01/2014, Las Vegas, Nevada, USA. pp. 359-360.
Descripción
| Título: | Region-based moving object detection using spatially conditioned nonparametric models in a GPU |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | IEEE International Conference on Consumer Electronics (ICCE 2014) |
| Fechas del Evento: | 10/01/2014 - 13/01/2014 |
| Lugar del Evento: | Las Vegas, Nevada, USA |
| Título del Libro: | IEEE International Conference on Consumer Electronics (ICCE 2014) |
| Título de Revista/Publicación: | 2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) |
| Fecha: | 2014 |
| ISSN: | 2158-3994 |
| Número: | null |
| Materias: | |
| ODS: | |
| Escuela: | E.T.S.I. Telecomunicación (UPM) |
| Departamento: | Señales, Sistemas y Radiocomunicaciones |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.
Más información
| ID de Registro: | 36200 |
|---|---|
| Identificador DC: | https://oa.upm.es/36200/ |
| Identificador OAI: | oai:oa.upm.es:36200 |
| URL Oficial: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb... |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 04 Jul 2015 07:34 |
| Ultima Modificación: | 04 Jul 2015 07:34 |
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